Health‐care resource use among patients who use illicit opioids in England, 2010–20: A descriptive matched cohort study
Bibliographic record
Abstract
BACKGROUND AND AIMS: People who use illicit opioids have higher mortality and morbidity than the general population. Limited quantitative research has investigated how this population engages with health-care, particularly regarding planned and primary care. We aimed to measure health-care use among patients with a history of illicit opioid use in England across five settings: general practice (GP), hospital outpatient care, emergency departments, emergency hospital admissions and elective hospital admissions. DESIGN: This was a matched cohort study using Clinical Practice Research Datalink and Hospital Episode Statistics. SETTING: Primary and secondary care practices in England took part in the study. PARTICIPANTS: A total of 57 421 patients with a history of illicit opioid use were identified by GPs between 2010 and 2020, and 172 263 patients with no recorded history of illicit opioid use matched by age, sex and practice. MEASUREMENTS: We estimated the rate (events per unit of time) of attendance and used quasi-Poisson regression (unadjusted and adjusted) to estimate rate ratios between groups. We also compared rates of planned and unplanned hospital admissions for diagnoses and calculated excess admissions and rate ratios between groups. FINDINGS: A history of using illicit opioids was associated with higher rates of health-care use in all settings. Rate ratios for those with a history of using illicit opioids relative to those without were 2.38 [95% confidence interval (CI) = 2.36-2.41] for GP; 1.99 (95% CI = 1.94-2.03) for hospital outpatient visits; 2.80 (95% CI = 2.73-2.87) for emergency department visits; 4.98 (95% CI = 4.82-5.14) for emergency hospital admissions; and 1.76 (95% CI = 1.60-1.94) for elective hospital admissions. For emergency hospital admissions, diagnoses with the most excess admissions were drug-related and respiratory conditions, and those with the highest rate ratios were personality and behaviour (25.5, 95% CI = 23.5-27.6), drug-related (21.2, 95% CI = 20.1-21.6) and chronic obstructive pulmonary disease (19.4, 95% CI = 18.7-20.2). CONCLUSIONS: Patients who use illicit opioids in England appear to access health services more often than people of the same age and sex who do not use illicit opioids among a wide range of health-care settings. The difference is especially large for emergency care, which probably reflects both episodic illness and decompensation of long-term conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".